- 1st week: Introduction to R and RStudio
- 2nd week: Data structure
- 3rd week: Graph I
- 4th week: Data manipulation I
- 5th week: Data manipulation II
- 6th week: Graph II
- 7th week: RMarkdown
- 8th week: Midterm exam
2018 3 8
https://spectrum.ieee.org/computing/software/the-2017-top-programming-languages
GUI for R
Free!
Easy to use
packages> tools
Make RMarkdown file: later
for i in [1, 2, 3, 4, 5]: print(i)
## 1 ## 2 ## 3 ## 4 ## 5
pwd python --version
## /Users/kwangyeolpark/Dropbox/WorkingWithMyself/강의/SW중심대학/2018_1Q2Q_lecture ## Python 2.7.10
1 + 3
## [1] 4
a <- c(100, 234, 356, 477, 888) mean(a)
## [1] 411
sd(a)
## [1] 301.2308
qplot(wt, mpg, data = mtcars)
ggplot(mtcars, aes(x = hp, y = mpg)) + geom_point(aes(color=factor(gear))) + facet_wrap( ~ cyl)
demo(graphics)
CityPopularity$Mean=mean(CityPopularity$Popularity)
CC <- gvisComboChart(CityPopularity, xvar='City',
yvar=c('Mean', 'Popularity'),
options=list(seriesType='bars',
width=450, height=300,
title='City Popularity',
series='{0: {type:"line"}}'))
plot(CC)






The connection between dataset and tables/graph might be broken easily.
Writing methods section based on the analysis you did 3 months ago.
Repetitive analyses are boring!
Hi Dr. Park,
I have starting working on GPBB manuscript (ASH as well).
I need a paragraph from you describing the statistical methodology you used when you analyzed the data for the ISC abstract a few years ago.
Can you also send me the list of final study cohort (300 patients) to me?
Thanks,

Cleaning data + Analysis + Writing